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International Journal of Mechanical and Materials Engineering (IJMME), Vol.6 (2011), No.1, 51-66

FUZZY LOGIC CONTROLLER FOR PREDICTING WIND TURBINE POWER

GENERATION

O. Badran a , E. Abdulhadi b and Y. El-Tous ca Mechanical Engineering Department,Faculty of Engineering Technology, Al-Balqa Applied University P.O.Box

331006, Amman 11134 Jordan b Mechatronics Engineering Department,Faculty of Engineering Technology, Al-Balqa Applied University P.O.Box

331006, Amman 11134 Jordan c Electrical Engineering Department,Faculty of Engineering Technology, Al-Balqa Applied University P.O.Box 331006,

Amman 11134 JordanEmail: email: [email protected]

Received 4 June 2010, Accepted 8March 2011

ABSTRACT

The utilization of wind energy for power generation purposes is occupying a great share in the electricalmarket worldwide and becoming increasingly attractive.The good exploitation of wind energy may enhance the

power generation capabilities, maximize its capacityfactor, and participate in generating electricity at goodcosts. The present paper comprises two parts; the first

part utilizes fuzzy logic methodology to assess wind sitesin Jordan and to decide which sites should be given thehighest priority with respect to their benefits and costs,and to predict the annual generation for different turbinesin the best sites. The criterion of evaluation using fuzzylogic is based on different parameters, i.e., windresources, prevailing wind direction, above ground level(AGL), site capacity, soil conditions, site elevation, landcost, land roughness, temperature, cultural andenvironmental concerns, aviation/telecommunicationsconflicts, nearby resident's concerns, site environmentalissues (corrosion, humidity), and distance to transmissionline. In the second part, a MATLAB/Simulink model isdeveloped to study the parameters that affect the powergeneration by wind turbines. The considered parametersare turbine swept area, air density, wind speed, and

power coefficient as a function of pitch angle and bladetip speed. The results show that Al Harir site in Al Tafilacity is found to be the best choice and should be given

the highest priority. It is followed by Al-Fujeij in Al-Shawbak then Al-Kamsha in Jerash.

Keywords : Wind energy, Fuzzy logic, Wind turbines,Site assessment.

1. INTRODUCTION

Today, new renewable resources provide only a smallshare of global energy production (Figure 1). However,the global market for wind power has been expandingfaster than any other sources of renewable energy. Theworld wind power capacity has been duplicated around

twelve times from just 4,800 MW in 1995 to reach overthan 59,000 MW at the end of 2005 (Figure 2).

Hence wind energy technologies are still maturing; performance is becoming better and more reliable whileat the same time costs continue to decline. Wind power isnow established as an energy source in over 50 countriesaround the world. Those with the highest totals in 2005were Germany (18,428 MW), Spain (10,027 MW), theUSA (9,149 MW), India (4,430 MW) and Denmark(3,122 MW). A number of other countries, includingItaly, UK, Netherlands, China, Japan and Portugal, havereached the 1,000 MW of wind power capacity (Figure 3)(Global Wind Energy Council, 2006). Denmark is

planning for wind power to cover 50% of the electricityconsumption by 2025 (American Wind EnergyAssociation, 2004).

Jordan is heavily reliant on energy imports, placing alarge annual burden on the national economy. Energyconsumption is growing at 5.5 % annually and electricitydemand growth is closer to 7.5 %. As part of its drive toreduce reliance on oil imports, Jordan has formulated

plans to accelerate the expansion of wind energy reliancein electricity consumption.

The government has set a target of acquiring five per centof total energy needs from renewable energy, includingwind energy, by 2015 (MEMR, 2006). Many studieshave been made to promote the wind energy throughoutthe world. Rosado et al (2008) used decision support

technology to promote the new wind farms by selecting asuitable geographical locations in Spain, their researchwas devoted to overcome the problems posed by thedifferent agents such as, investors, utilities, governmentalagencies or social groups that could delay the planning

process. Mguez et al (2006) studied the possibility ofincreasing the share of wind power generation by 51% ofthe total capacity size in Galicia (Spain) and showed its

benefits from the economic and environmental point ofview.

A promotional study has been prepared by Feller (2007)to help remove policy, regulatory, and financial barriers

to the promotion of wind power in Jordan.Another study has been proposed by Hrayshat (2007 a) toassess the wind in southern region of Jordan for water


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pumping applications using wind turbine. Badran (2003)also reviewed the wind turbine utilization for water-

pumping applications to meet the energy requirementsfor remote villages, settlements, and farms in Jordan.

The interest and motivation for utilizing wind power

have grown tremendously during the nineteen-eighties in

many developed and developing countries (i.e. Jordan),as a result of frequent energy crises on one hand and

persisting issues of environmental pollution on the other.These activities have inspired many researchers andscientific community in Jordan to investigate the wind

potentials in Jordan.

Figure 1 Fuel shares of world total primary energy supply (IEA, 2007)

Figure 2 Global cumulative wind power capacitySource: (Global Wind Energy Council, 2006)

Figure 3 The greatest global wind capacity shareSource: (Global Wind Energy Council, 2006)


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The first wind speed measurement recorded in Jordanwas in January 1923 at the Amman civil airport and was

by the British Air Force. It lasted for one year andmeasured only the maximum wind gust according to theLustrum forms filed by the Jordan MeteorologicalDepartment (JMD). The measurements were restarted in

June 1948, taking into account all relevant climatic parameters. Wind measurements, however, were taken asan average of three readings in each day. Averages weretaken over each indicated hour, and the three hourly

points were averaged to obtain the mean wind speed forthat day. Then, the JMD was relocated in 1952 as a

branch of the Civil Aviation Authority. Later a law wasissued declaring the JMD as an independent departmentin 1967. The JMD continued to measure wind speedusing the above mentioned procedure. However, in 1977,they started taking four daily readings instead of three.Furthermore, the newly installed sensors were at a heightof 10 m. This method of measurement is still followed

today. Up to 1983, there were some meteorologicalstations in Jordan operated independently of the JMD bythe Natural Resources Authority (NRA) and the JordanValley Authority (JVA). The Royal Social Society (RSS)installed accurate measuring instruments since the middleof eighties to determine the wind energy resources ofdifferent sites in Jordan. They have installed 14 dataacquisition systems for measuring wind speed anddirection in various geographical locations throughoutthe country (Taani, 1986 ; RSS, 1994). In 1982, theMechanical Engineering Department at the University ofJordan started theoretical studies on wind energy. Thesestudies dealt with wind energy evaluation, economic

feasibility of wind energy applications in Jordan andaerodynamic design of conventional and non-conventional wind energy converters. Wind energyaugmentation principles and wind farms were alsoinvestigated (Habali et al, 2001; RSS, 1994; Badran,2003). Many studies have been proposed to optimizewind turbine power generation. Shata and Hanitsch(2006) have evaluated different wind sites in Egypt usingclassical statistical analysis to estimate the best wind site.Koak (2008) focused entirely on wind speed persistenceduring weather forecast, site selection for wind turbinesand synthetic generation of the wind speed data. Herbertet al 2007 developed models for wind resources

assessment, site selection and aerodynamic includingwake effect to improve the wind turbine performance andto increase its productivity. Lackner et al (2008) utilizedground-based measurement devices instead ofmeteorological towers for wind resource assessment.They investigated the use of a monitoring strategy inmore than one site to determine the best wind sites. Thecumulative avoided CO 2 emission will be 3,375 milliontones by 2020 if the global wind power capacityincreased to 230,658 MW (Global Wind Energy Council,2006; Saidur et al., 2007; Hasanuzzaman et al., 2008).Also Saddler et al (2007) showed that increasing theshare of renewable power generation, including wind

power, could result in 78% in CO 2 emission reduction inAustralia.

There are many attempts made to improve the windturbine itself; Irabu and Roy (2007) proposed a study toimprove and adjust the output power of Savonius rotorunder various wind power and suggested a method of

preventing the rotor from strong wind disaster.Khalfallah and Koliub (2007) focused entirely on the

turbine rotor and blades in order to improve the windturbine power curves. Sutikno (2011) used two stages blades contra rotation to enhance the speed regulation ofthe wind turbine. The present study utilizes fuzzy logic toevaluate various sites in the bases of their benefits andcosts to adopt the higher priority sites in terms ofdifferent criteria, i.e., wind resources, prevailing winddirection, above ground level (AGL), site capacity, soilconditions, site elevation, land cost, land roughness,temperature, cultural and environmental concerns,aviation/telecommunications conflicts, nearby resident'sconcerns, site environmental issues (corrosion,humidity), and distance to transmission line. Also itshows the factors that affect the wind power over wideranges, which will lead the wind developers andresearchers to focus on the highest priority parametersthat should be considered for installation andmanufacturing of the new generations of wind turbines.The parameters that affect the power generated by windturbines can be classified into two categories, namely, thesite in which the turbine will be installed, and the windturbine itself as shown in Figure 4.

Figure 4 Classification of factors that affect wind powergeneration

2. SITE SELECTION

The most promising sites in Jordan have been consideredin terms of their benefits and costs (barriers). The


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selected areas are located in the northern and southernregions of Jordan, they represent moderate-to-good windresources (Ministry of Energy and Mineral Resources,1996). According to the National Renewable EnergyLaboratory (NREL) the wind resource classification of6.4-7.5 m/s is classified beneath moderate-to-good wind

resources (Young and Vilhauer, 2003)

The assessment criterion is classified into two maincategories, namely, benefits and costs. Benefits representthe different parameters that enhance the site's credibilitywhile costs characterize the factors that affect thesuitability of the site and the installation cost.

The "benefit" parameters are wind resources, prevailingwind direction, turbine height (above ground level(AGL)), site capacity (compatibility with expansion

plans), soil conditions, and site elevation, while the"cost" parameters are: land cost, land roughness for

availability of rough terrain equipments, temperature,cultural and environmental concerns,aviation/telecommunications conflicts, nearby resident'sconcerns, site environmental issues (corrosion,humidity), and distance to transmission line (Young andVilhauer, 2003; Wind Resource Assessment Handbook,1997).

2.1. Selected Sites

The sites that have been considered are located in Al-Fujeij near Al-Shawbak city, Al-Harir near Al-Tafilacity, and Wadi Al-Aqaba near Aqaba city (southern

region), and Al-Kamsha near Jerash city (Northernregion). The collected data for these sites were takenfrom 1996 to 2005. The average temperature, humidityand prevailing wind direction were monthly averaged forthe whole period (1996~2005) (MeteorologicalDepartment, 1996-2005), they will be discussed asfollows:

Al-Fujeij site:

Al-Fujeij site lies to the north east of the city ofShawbak, in the area of Maan. The land is owned by theMinistry of Agriculture. The site is bounded by a petrolfilling station and arable farm land. It is 1200-1250 m

above the sea level (Figure 5 and Figures 6a and b)(Google Earth, 2008).

It has rocky desert soil nature (Figure 5 and Figure 6),and located 11 km away from transmission lines andsubstations (Figure 7a) (PB POWER, 2007).

The region has some environmental and culturalconcerns sense it lies on flight line for migratory birds,and it is home to a number of archaeologically interestingsites and old cultural periods, namely, Roman (64 BC),Byzantine (333 BC), Nabatean (200 BC) (PB POWER,2007).

Regional wind resource is considered the most importantfactor in site selection; the regional wind resources for

the selected regions have been estimated from the windresource map of Jordan as shown in figure 7b (Ministryof Energy and Mineral Resources, 1996). Al-Fujeij

possesses good wind resources as shown in Figure 7b,the average wind speed is estimated (7.2- 7.5 m/s) at hubheight 50 m (Ministry of Energy and Mineral Resources,

1996). The prevailing wind direction varies between(217 o and 283 o) as in Fig 8. The average temperaturevaries between (14 35oC) (Figure 9). The mean relativehumidity varies between (41% and 68%) as shown inFigure 10 (Meteorological Department, 1996-2005). Itcan be seen from Figs 8 to 10 that the wind direction forAl-Fujeij site is fluctuating during the summer months(June to August); this corresponds to higher temperatureand lower average humidity.

Al-Harir site

Al-Harir is located near Tafilah city in the southern

region of Jordan. As can be seen from Figs 6 a and b andFig 11, the terrain is dry and it is 1300-1350 m above thesea level.

The region possess a good wind resources, it has around7.2 m/s average speeds as shown in Figure 7 b (MEMR,1998-2002). It is located about 3 km away from theclosest substation (Figure 7a). The site has good

prevailing wind direction which varies between 240 o and260 o (Figure 8) that would decrease the turbines spacingand increase the turbines life due to the continuous winddirection tracking. This site has the lowest averagetemperature, it varies between 11 to 32 oC throughout the

year (Figure 9), and lowest relative humidity between30% and 55% (Figure 10), and that may reduce theenvironmental concerns like corrosion, which wouldlengthen the turbine's life.

Al-Kamsha site

Al-Kamsha site is near Jerash city located in the northernregion of the country. Its land nature is scrubby andwoody (Figure 6 b and Figure 12). It is about 650-700 mabove the sea level (Figure 6a). The site is bounded bymilitary base, which can limit the expansion plans(MEMR, 2008). It has moderate-to-good wind resources

6.1-6.3 m/s (Figure 7b).

The prevailing wind direction in the site varies between170 o and 280 o as shown in Figure 8. The averagetemperature varies between 14 and 33 oC while the meanrelative humidity varies between 55% and 78% (Figs. 9and 10) (Meteorological department, 1998-2005).

There are large number of old monuments located inJerash and the land surrounding it belongs to the Roman

province in 63 BC which may cause some culturalconcerns that should be considered during the siteselection (Ministry of Tourism & Antiquities, 2003).


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Figure 5 Al-Fujeij satellite map view from 10 Km (Source: Google earth)

(a) (b)Figure 6 Jordan maps (a) Topography (b) Vegetation and precipitation

(a) (b)

Figure 7 Jordan maps (a) Transmission lines and substations (Source: NEPCO), (b) Wind map resources (Ministry ofEnergy and Mineral Resources, 1998-2002)


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Figure 8 Prevailing wind direction for the selected cites

Figure 9 Average temperatures for the selected sites

Figure 10 Annual average humidity for the selectedsites

Wadi Al-Aqaba site

Wadi Al-Aqaba is located near Aqaba city in thesouthern region of Jordan. Aqaba is considered a hometo number of archaeologically interesting sites and oldculture since it was a center of the Edomites, and thenof the Arab Nabataeans. It is strategically important toJordan as it is the country's only sea port. The selectedsite is about 600-650 m above the see level and it hassemi-desert nature (Figure 6b and Figure 13). It hasmoderate-to-good wind resources of about

Figure 11 Al-Harir satellite map view from 10 Km (Source: Google earth)

6.6 m/s average speed (Figure 7b) (Badran, 2006).Also It is has relatively high average temperature andhigh relative humidity (Figs 9 and 10).

The specific power (power per m 2) for a wind site can be determined from equation 1; (Danish Wind IndustryAssociation, 2003):

Specific power = 1/2 v 3 (1)

Where P is the power of the wind measured in W(Watt). (rho) is the density of dry air = 1.225measured in kg/m 3 (at average atmospheric pressure atsea level and 15 C). v is the velocity of the windmeasured in m/s.

Figures 14 a and b compare between the different sites

in Jordan in the basis of their average wind speed,specific power and elevation.


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It is very important for the wind turbines industry toestimate the specific power and the variation of windspeeds in the sites for estimating the needed poweroutput. Also turbine designers need the detailedinformation to optimize the design of their turbines tominimize the generating costs. The Weibull

distribution (Figure 15) shows a probability densitydistribution of wind speed at different topographies inJordan, as can be inferred from the figure 15, the widerdistribution at higher velocities occurred in the hillyareas, followed by the desert regions.

Figure 12 Al-Kamsha satellite map view from 10 Km (Source: Google earth)

Figure13 Wadi Al-Aqaba satellite map view from 10 Km (Source: Google earth)


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(a) Average wind speed and specific power

(b) Site elevation

Figure 14 Comparison of the four sites

2.2. FUZZY METHODOLOGY

Fuzzy logic has a wide range of utilizations in decisionmaking since it condenses a large amount of

parameters into smaller fuzzy sets. The currentimplementation uses the fuzzy steps as follows:

1- Determining the linguistic variables and the fuzzysets.

The fuzzy logic decision selection (FLDS) of the sites options was applied according to benefits (Figure 16),namely, (B1= Wind resource based on Jordan windmap, B2= Prevailing wind direction, B3= Aboveground level (AGL) (m), B4= Site capacity, B5= Siteaccessibility, B6= Soil conditions, B7= Site elevation)to make decision selections between different sites that

cost less and have much benefits, many factors affectdecision (i.e. costs) (Figure 17), they include (C1=Land cost, C2= Land roughness, C3= Temperature(oC), C4= Cultural and environmental concerns, C5=Aviation/Telecommunications conflicts, C6= Nearbyresident's concerns, C7= Site environmental issues

(corrosion, humidity), C8= Distance to transmissionline (m)).

Figure 15 Weibull distribution of wind speed in Jordan

The Fuzzy input/ output combination is shown in theFigure 18.

The fuzzy logic methodology is applied taking intoaccount each site parameters as shown in Tables 1. and2. Data in Tables 1 and 2 are estimated based on thedata provided in section (2.1).

The inputs in tables 1 and 2 are considered to be thefuzzy variables, each variable can vary over a fixedrange, and the inputs' and output's sets are either threefuzzy sets type or five fuzzy sets type as shown inFigure 19. The linguistic variables used in the fuzzymethodology are: Very low (VL), Low (L), Normal(N), High (H), Very high (VH), Poor, Marginal,Satisfactory, Good, Excellent, Upgradeable, Solid rock,Fractured rock, Rock/soil, Soil/rock, All soil, Moderate

None, Extensive, Minor, Average, Very close, Close, Not far, Far, Very far. Each fuzzy set is addressed aslisted in table 3.

Figure 16 Benefits fuzzy structures


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Figure 17 Costs fuzzy structures

Figure 18 Fuzzy inputs/outputs combination

Table 1 Overall fuzzy weights for the selected sites based on benefits

B1 B2 B3 B4 B5 B6 B7 ** Relativeweight

Normalizedrelativeweight*

Parameter's weight 1 0.4 0.4 0.4 0.4 0.4 0.4 - -Al Harir 0.9 1 50 1 0.8 0.5 1331 0.814 1.000Fujeij 1 0.7 50 0.8 0.9 0.3 1236 0.737 0.905Wadi Aqaba 0.8 0.8 50 0.9 1 0.4 649 0.725 0.891Kamsha 0.7 0.45 50 0.25 0.8 0.9 697 0.651 0.800

* Normalized relative weight = relative weight/maximum relative weight** B: Benefit

Table 2 Overall fuzzy weights for the selected sites based on costs

C1 C2 C3 C4 C5 C6 C7 C8 Relativeweight

Normalizedrelativeweight*

Parameter's weight 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 - -Al Harir 0.8 0.1 22.4 0.3 0.1 0.1 0.3 3 0.292 0.605Fujeij 0.2 0.1 25 0.65 0.1 0.4 0.5 11 0.394 0.816Wadi Aqaba 0.4 0.5 31 0.65 0.1 0.5 0.75 3 0.483 1.000

Kamsha 0.2 0.3 23 0.8 0.1 0.6 0.8 3 0.42 0.87* Normalized relative weight = relative weight/maximum relative weight**C: Cost


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Figure 19 Input/Output fuzzy sets

Table 3 Fuzzy sets

Variabletype

Fuzzy sets

Main parameters

Symbol 1 2 3 4 5 Range Unit

Wind resource B1 Input VL L N H VH 0-1 -Wind direction B2 Input Poor Marginal Satisfactory Good Excellent 0-1 -AGL B3 Input VL L N H VH 10-70 mSite capacity B4 Input Poor Marginal Satisfactory Good Excellent 0-1 -Siteaccessibility

B5 Input Poor Marginal Upgradeable Good Excellent 0-1 -

Soil conditions B6 Input Solidrock

Fracturedrock

Rock/soil Soil/rock All soil 0-1 -

Site elevation B7 Input VL L Moderate H VH 0-1500 mLand cost C1 Input L N H 0-1 -Landroughness

C2 Input VL L N H VH 0-1 -

Temperature C3 Input L N H - - 15-35 oCCulturalconcerns

C4 Input None Moderate Extensive - - 0-1 -

Tele-communicationconcerns

C5 Input None Moderate Extensive - - 0-1 -

Residentsconcerns

C6 Input Low Moderate Extensive - - 0-1 -

Environmentalconcerns

C7 Input None Minor Average Moderate Extensive 0-1 -

Distance totransmission

C8 Input Veryclose

Close Not far Far Very far 0-20 km

B Output VL L N H VH 0-1 -C Output VL L N H VH 0-1 -

2- Constructing fuzzy rules.

Sixty five rules were used in the current fuzzy methodimplementation to predict the most preferable options oroption out of the four sites; they are represented instatements form as shown in Figure 20. Fuzzy logicenabled us to dense large amount of data, collected tocompare between different sites, into a smaller set of

variable rules (see Tables 1 and 2), to make a decision inthe basis of their merits and barriers to produce higher

power output at low cost as well as to capture asmaximum wind power as possible.

The benefit to cost ratio is shown in Table 4.


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Figure 20 Fuzzy rules

Figure 21 Fuzzy implementation sequence

Figure 22 Comparison between normalized benefits costsand benefits to cost ratios

3- Performing fuzzy inference into the system.

Finally, the mapping process takes place to provide thefinal decision as shown in Figure 21. Fuzzification turnsnumeric values into linguistic descriptions (VL, L, N, H,and VH). This process is accomplished by evaluating themembership functions (MFs) with respect to the input

value in order to establish the degree of activation ofeach output membership function.

At the end of this process, list of activations are obtainedand can be carried forward to the next stage (aggregationsub-process). In the aggregation sub-process, the effects

of each rule on the possible output conditions areaccumulated. Defuzzification carries out the estimationof the outcomes of the inference processes. Each outputvariable is analyzed separately as shown in Figure 21.

2.3. RESULTS AND DISCUSSION

As can be seen from Table 4 and Figure 22, Al-Harir siteshowed the highest benefit to cost ratio. Therefore, itshould be given the highest priority in terms of installingwind turbines to achieve high power production capacityfor supplying nearby grid lines and for the electrificationof small arid communities. The second and the third are,

Al-Fujeij and Al-Kamsha, respectively.

Table 5 shows the list of different turbines that may beerected for producing electricity (i.e. annual generationand estimated levelized cost), at the best chosen site (Al-Harir). The economical considerations are the mostimportant aspect in selecting the proper technology to beused in any project and location. If appropriate location ischosen, wind energy will be economically viable optionfor the production of electricity. A major considerationin the assessment of different wind turbines viability inJordan, is the analysis of the cost of energy or electricity

produced by the system (Table 5). Power plants are

compared on the basis of their Levelised Electricity Cost(LEC), which depends on the capital cost of the plant,annuity factor, and annual operation and maintenancecosts to the annual production of electricity (Equation 2),the wind turbine life should be assumed (30 years).Equation 2 is used for calculating the LEC:

Levelized Electricity Cost (LEC) =el P

M OCC a &

(2)Where:CC: capital cost [$], the raw equipment of wind turbine is

estimated with $750/rated kW which represents about80% of the total capital cost. Other costs representfoundation, grid connection lines, electrical installations,communication, land roads, consulting services,(Edelstein et al, 2003) (NWCC, 1998) (Smith, 2001)(Fingersh et al, 2006).

a: annuity factor, assumed to be 19%.O&M: annual operating and maintenance expenditure[$], evaluated with 0.015$/kWPel: yearly electricity production [kWh]

The results from the recent study have shown that wind

electricity generation cost ranges between 7.35 to 7.8cents/kWh depending on benefits and costs factorsdiscussed earlier (Tables 1-2 and 4-5).


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Table 4 Benefit to cost ratio

Site Normalized benefitsrelative weight (Table 1) Normalized costs relative

weight (Table 2) B/C Normalized

B/CAl Harir 1.000 0.605 1.653 1.000

Fujeij 0.905 0.816 1.109 0.671Wadi Aqaba 0.891 1.000 0.891 0.539

Kamsha 0.800 0.87 0.92 0.57

The final results obtained are shown in Figure 22.

Table 5 Annual generations for Al-Harir site for different turbines model

Existingturbines

Hubheight

[m]

Averagewindspeed[m/s]

Rotordiameter

[m]

Sweptarea [m 2]

Requiredspecific power[W/m 2]

Sitespecific power[W/m 2]

Max power[MW]

Capacityfactor

*Annualgeneration

[MWh]

Estimatedlevelized

cost[Cent/kWh]

Nordex 50 7.2 54 2290.2 440 440 1 0.32 2803.200 6.36Enercon 50 7.2 66 3421.19 440 440 1.5 0.27 3547.800 7.53 Nordex 60 7.41 60 2827.4 460 475 1.3 0.3 3416.400 6.78Kvaemer 100 8 80.5 5089.57 590 600 3 0.26 6832.800 7.82

*Annual production = Max power * Capacity factor * 8760

3. PARAMETERS THAT AFFECT WIND

TURBINE GENERATION

In the previous sections, the factors that affect theselection of the wind site have been discussed. Thecurrent section discusses the factors that affect the windturbine generation in the site.

Wind machines were used long time ago, the very firstelectricity generating windmill operated in the UK was a

battery charging machine installed in 1887 by JamesBlyth in Scotland. The first utility grid-connected windturbine operated in the UK was built by the John BrownCompany in 1954 in the Orkney Islands.

Wind turbines are designed to exploit the wind energythat exists at a location. Virtually all modern windturbines convert wind energy to electricity for energydistribution. The modern wind turbine is a system thatcomprises three integral components with distinctdisciplines of engineering science. The rotor component,which is approximately 20% of the wind turbine cost,includes the blades for converting wind energy to anintermediate low speed rotational energy. The generatorcomponent, which is approximately 34% of the windturbine cost, includes the electrical generator, the controlelectronics, and most likely a gearbox component forconverting the low speed rotational energy to electricity.The structural support component, which isapproximately 15% of the wind turbine cost, includes thetower for optimally situating the rotor component to thewind energy source (Fingersh et al, 2006).

Wind turbines are classified, in the basis of their axis inwhich the turbine rotates, into horizontal axis and verticalaxis wind turbines. Because of the ability of thehorizontal axis turbines to collect the maximum amountof wind energy for the time of day and season and toadjust their blades to avoid high wind storms; they areconsidered more common than vertical-axis turbines(Danish Wind Industry Association, 2003).

Turbines that used in wind farms for commercial production of electric power nowadays are usually three- bladed and pointed into the wind by computer-controlledmotors. This type is produced by the most common windturbines manufacturers. The top ten worldwide windturbines manufacturers are shown in table 6. (Efiong andCrispin, 2007).

A wind turbine captures energy from moving air andconverts it into electricity. The captured energy isaffected by factors such as air density, turbine sweptarea, air velocity and power coefficient as in thefollowing equation (Mukund, 1999):

AV C P p ....5.0 3

(3)

Where, P is the mechanical power from the moving air,Watt; air density, kg/m; A area swept by the rotor

blades, m; V velocity of the air, m/s; C p powercoefficient.

A MATLAB/Simulink model (Figure 23) is developed toshow how these factors affect the generated power fromwind turbine.


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Table 6 Top ten wind turbines manufacturers in 2006

AccumulatedMW 2005

Supplied MW2006 Share 2006 %

AccumulatedMW 2006

Shareaccumulated %

Vestas 20,766 4,239 28% 25,006 34%Gamesa 7,912 2,346 16% 10,259 14%

GE Wind 7,370 2,326 15% 9,696 13%Enercon 8,685 2,316 15% 11,001 15%Suzlon 1,485 1,157 8% 2,641 4%

Siemens 4,502 1,103 7% 5,605 8% Nordex 2,704 505 3% 3,209 4%Repower 1,522 480 3% 2,002 3%Acciona 372 426 3% 798 1%

Goldwind 211 416 3% 627 1%Others 6,578 689 5% 7,267 10%

Figure 23 MATLAB/Simulink model

The Simulink model is valid for wide ranges of windturbines. It is tested on VESTAS V52 wind turbine forvalidation purposes. The VESTAS V52 wind turbineshave been erected in many countries more than any otherturbines in VESTAS portfolio, approximately 2,100turbines installed all over the world due to their highlyefficient operation and flexible configuration. The V52specifications are shown in table 7. (VESTAS windsystems, 2000).

Table 7 Specifications of VESTAS V52 wind turbine

Rotor diameter 52mArea swept 2,124 m 2

Number of blades 3Power regulation Pitch/OptiSpeedAir brake Full blade pitchCut-in wind speed 4 m/s

Nominal wind speed 16 m/sCut-out wind speed 25 m/s

Nominal output 850 kW

Figure 24 Effect of swept area on the generated power

The results showed that the mechanical power increaseswhen the turbine's swept area increase as shown inFigure 24. The temperature and the air density impact onthe generated power are shown in Fig 25. It can be seenfrom figure 25, that the ambient temperature in the site


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has a direct effect on the wind power, as the temperatureincreases the power decreases, this is due to a decrease inthe density of air, which affect the air flow movements.

The power variation with the wind speed and powercoefficient is shown in Figure 26. Turbine power

increases as the wind speed increases, putting more

momentum on blades by air up to certain range then the power decreases, due to the turbulence effect of airaround blades at high wind speeds, which creates stall on

blades. The power coefficient follow same trend as total power but high values occur at lower wind speeds.

Figure 25 Effect of air density-temperature on the generated power

Figure 26 Generated power as a function of the powercoefficient

3.1. RESULTS AND DISCUSSION

Wind turbines should be optimized, by taking swept area

into consideration, in terms of the local area conditions tocapture power as maximum as possible. As can be seenfrom Figure 24, the output power of a wind turbine isdirectly related to the wind speed as well as to the sweptarea of blades. The larger the diameter of its blades, themore power can be extracted from the wind. The power

produced by the wind turbine increases from zero at thethreshold wind speed (cut in speed) (usually around 5m/s

but varies with site) to the maximum at the rated windspeed. Above the rated wind speed, (15 to 25 m/s) thewind turbine continues to produce the same rated power

but at lower efficiency until shut down is initiated if thewind speed becomes dangerously high, i.e. above 25 to

30m/s (gale force). The exact specifications for energycapture by the turbine depend on the distribution of windspeed over the year at the individual site. Air density hasa significant effect on wind turbine performance (Figure

25). The power available in the wind is directly proportional to air density. As air density increases theavailable power also increases. Air density is a functionof air pressure and temperature. It increases when air

pressure increases and the temperature decreases. Bothtemperature and pressure decrease with increasingelevation. Consequently changes in elevation produce a

profound effect on the generated power as a result of theair density change.

4. CONCLUSION

It can be concluded that the advantages of the fuzzy logicmethodology for evaluating different sites in Jordan

presented in this study enabled us to condense largeamount of data into smaller set of variable rules. Basedon the results of fuzzy logic methodology, Al-Harir sitehas the greatest wind potential and the best wind powergeneration output, and it should be given the highest

priorities in terms of installing the maximum number ofwind turbines. Different wind turbine types have been

proposed to meet the target demand and to find out the best turbine suited to the highly preferred site for futureerection of wind farms. In conclusion, many factors haveto be considered during manufacturing or installation ofwind turbines, i.e., (turbine swept area, air density, windspeed, and power coefficient as a function of pitch angleand blade tip speed).

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